1. Field of the Invention
One or more embodiments of the invention are related to the field of computer systems. More particularly, but not by way of limitation, embodiments of the invention enable an enhanced search system and method based on entity scoring and ranking configured to provide improved searches through calculation of scalar online and offline user rankings quantified by peer-to-peer voting that are extended through network analysis. In one or more embodiments of the invention, searches for people and information, for example, may employ and enhance offline or physical-world reputation-enabled entity scoring and rankings in online environments and vice versa, which is unknown in the art.
2. Description of the Related Art
Currently known search providers generally do not automatically take into account the preferences of an information searcher beyond that searcher's search request, or the reputation of the sources, which offer the information that is being searched. As a result, not only are searchers offered only limited means for leveraging personal preferences throughout the search process, but they are also unable to conduct searches based on the reputation of information sources or rank information sources based on reputation. A searcher may have some familiarity with certain sources and thus a subjective impression of the sources' reputations or their alignment with the searcher's preferences. However, search providers generally do not offer searchers any means for assessing a large variety of sources whose reputations are objectively quantified and standardized in a way that would enable searchers to compare and rank sources based on specific preferences beyond the search request and/or based on source reputation. More specifically, common types of search providers such as certain Internet search engines sample online user behavior, for example, as represented by the number of searchers, recurring or non-recurring web site visits, page or advertising views, clicks, or “click-throughs”. Other Internet search engines utilize evaluations of the types and degrees of connectedness among sites (e.g. as represented through inbound and outbound links) to establish source relevance and quality. Therefore, the operative variables employed by known search engines are ultimately based on the behavior of many online users that is aggregated, evaluated, sorted, ranked, and presented back to users as search-facilitating criteria. Here, source reputation is implicitly relegated to the notion that quantity (e.g. the number of page visits or inbound or outbound links) reasonably approximates source relevance and quality. Moreover, search results are often also manipulated by so-called “search engine optimization” providers. Search engine optimization pursues strategic deployment of online information to improve search engine rankings for paying customers. Thus, inadequate information is commonly provided, thereby diminishing the usefulness of search results.
There are other search systems that establish some kind of source quality through various rating mechanisms. However, these mechanisms are limited in that they permit users to rate only instances of what other users say or do or look like or endorse or buy. The only online environments where users can currently acquire some sort of reputation are systems that track the volume of user activity (e.g. the quantity of information shared) across various social networking sites, or online marketplaces where users are rated primarily by strangers and only with respect to business trustworthiness, that is, the assessment of transaction fulfillment risk. In both settings reputation is confined to the very context in which it was acquired. There is no online place that mirrors the physical world in that people acquire a reputation first and foremost within their social circles and this reputation is then carried forward into the extended networks of their peers, driven by the impact of their peers' reputations. In other words, no online context exists that consistently enables a “real” reputation, or rather a perception of quality that attaches to a person's characteristics or abilities, and that transcends the division between physical and online world because it is tantamount to a personal brand.
As stated, known search systems take into account only narrowly defined types of reputation and largely ignore physical-world reputation. In the physical world, reputation can be defined as an entity's quality or characteristic or ability as perceived by another entity, whereas an entity can be defined as a person, or organization, or group, or object, or system, or concept affiliated or associated with a person, or organization, or groups, or systems, or conceptual representations thereof. Reputation is thus instrumental in shaping an entity's social and personal identity. It would therefore be desirable to provide a search system that defines reputation holistically, enabling the utilization of offline, or rather physical-world reputation in the online world, and providing ways to enhance physical-world reputation using online means. None of the many known Internet-enabled search systems achieve this objective because they employ a definition of reputation that is behavior-specific and tied to a certain context, rather than a definition of reputation that is entity-specific because it is tied to a certain entity's attributes or characteristics.
Collaborative filtering systems employ techniques that draw predictive conclusions based on behavioral patterns that are shared among users. For example, users may be presented with product suggestions that are informed by statistical algorithms extrapolating from past site-specific behavior by other, similar users. As such, search results generated from the perspective of collaborative filtering systems are associated with behavioral similarity among users who are otherwise unfamiliar with one another, and with respect to specific consumption contexts.
There are other online rating or review systems that also draw upon informational relevance of similarity in past or intended consumption behavior among users who are otherwise unfamiliar with one another. As such, reputation refers primarily to degrees of agreement among users with respect to certain qualities of usually unfamiliar third entities. Moreover, online ratings or reviews are frequently based on small samples of user votes because they tend to eschew user data aggregation beyond individual sites. In addition, people with extreme opinions are much more likely than average users to rate or review anything online and the underlying sample sizes are too small to overcome this inherent voting bias and the resulting statistical error, and generate a meaningful level of significance. These problems are exacerbated by anonymity, that is, users cannot easily determine the relevance of other users' ratings with regards to motivation or competence.
As mentioned, electronic marketplaces frequently implement systems that allow users to rate one another with respect to a preceding transaction. As such, search results are presented based on the interaction over time among many users who are otherwise unfamiliar with one another and across many transactions in one particular context.
Other, more sophisticated systems exist that are also predominantly focused on user behavior in electronic transactions. Some of these systems extend beyond a first and/or unilateral layer of evaluation sources by enabling weighted and/or reciprocal exchanges of user ratings. This means the impact of an entity's ratings is influenced by the entity's own rating. However, these systems are also site-specific and merely employ reputation as a transaction risk reduction tool that is based on the degree to which a multitude of users who are otherwise unfamiliar with one another agree on the ratings of other entities over time, and only with respect to certain electronic transactions such as the purchases of goods and services.
In other words, systems conceived to provide user value in electronic transactions or online marketplaces address only transactional fulfillment risk. As such, any related ranking of users would provide user value only for a narrow range of user interactions and for one particular place.
Social networking sites represent a departure from these anonymous single-purpose environments, as they cluster individuals connected through varying degrees of familiarity, often derived from or supplemented by interaction in the physical world. However, such sites entirely relegate the notion of reputation to the physical world, presumably because they assume that offline familiarity among users sufficiently establishes reputation. In addition, such sites also usually provide rudimentary mechanisms that allow users to vote on one another, that is, express agreement about one another's opinions or actions (e.g. through the use of “like buttons”) or social media influence, but not usually about user attributes. Also, these mechanisms do not generally allow anonymous voting or weighting and do not mandate reciprocity.
There also exist a multitude of social sites that connect individuals who do not usually know one another for the sole purpose of rating one another's phenotype.
As a result, currently known Internet-enabled systems do not offer holistic implementations of the features that make reputation valuable in the physical world:
Attribute specificity and diversity: addressing one or more specific qualities or characteristics or abilities.
Portability: applying across different environments and contexts (e.g. online and offline, as well as across different settings within each context) that are unified by the attributes under consideration.
Entity dependence: attaching to an entity's public or private persona or image or identity as supposed to an entity's actions in one context.
Weighting by source reputation: it matters whether an opinion about an entity is issued by a source with high or low reputation.
Reciprocity: entities evaluate one another along the same criteria.
Opinion heterogeneity: reputation formation derives from samples that are large enough and diverse enough to address possible statistical errors meaningfully.
For at least the limitations described above there is a need for an enhanced search system and method based on entity ranking.